Although column experiments are frequently used to investigate the transport of organic micropollutants, little guidance is available on what they can be used for, how they should be set up, and how the experiments should be carried out. This review covers the use of column experiments to investigate the fate of organic micropollutants. Alternative setups are discussed together with their respective advantages and limitations. An overview is presented of published column experiments investigating the transport of organic micropollutants, and suggestions are offered on how to improve the comparability of future results from different experiments. The main purpose of column experiments is to investigate the transport and attenuation of a specific compound within a specific sediment or substrate. The transport of (organic) solutes in groundwater is influenced by the chemical and physical properties of the compounds, the solvent (i.e., the groundwater, including all solutes), and the substrate (the aquifer material). By adjusting these boundary conditions a multitude of different processes and related research questions can be investigated using a variety of experimental setups. Apart from the ability to effectively control the individual boundary conditions, the main advantage of column experiments compared to other experimental setups (such as those used in field experiments, or in batch microcosm experiments) is that conservative and reactive solute breakthrough curves can be derived, which represent the sum of the transport processes. There are well-established methods for analyzing these curves. The effects observed in column studies are often a result of dynamic, non-equilibrium processes. Time (or flow velocity) is an important factor, in contrast to batch experiments where all processes are observed until equilibrium is reached in the substrate-solution system. Slight variations in the boundary conditions of different experiments can have a marked influence on the transport and degradation of organic micropollutants. This is of critical importance when comparing general results from different column experiments investigating the transport behavior of a specific organic compound. Such variations unfortunately mean that the results from most column experiments are not transferable to other hydrogeochemical environments but are only valid for the specific experimental setup used.
Column experiments are fast, flexible, and easy to manage; their boundary conditions can be controlled and they are cheap compared to extensive field experiments. They can provide good estimates of all relevant transport parameters. However, the obtained results will almost always be limited to the scale of the experiment and are not directly transferrable to field scales as too many parameters are exclusive to the column setup. The challenge for the future is to develop standardized column experiments on organic micropollutants in order to overcome these issues.
The presence of organic micropollutants in aquatic environments has been of great concern worldwide for a number of years and increasing numbers of compounds continue to be detected in all kinds of waterbodies. Contaminated drinking water can be a major source of human uptake of organic micropollutants. Since groundwater is widely used for drinking water supplies worldwide, it is of utmost importance to have a detailed understanding of the distribution and transport of these compounds in groundwater, in order to guarantee safe drinking water for mankind in the future. The World Health Organization's water safety plans also highlight the need for a profound understanding of the processes involved to protect groundwater as a drinking water resource (WHO, 2005).
Increasing numbers of different types of organic micropollutants have been identified and investigated in aqueous environments over recent decades, for example, pharmaceutical compounds (Kümmerer, 2009; Li, 2014; Richardson and Bowron, 1985), pesticides (Ritter, 1990; Song et al., 2010; Yadav et al., 2015), and hormones (Kolpin et al., 2002; Silva et al., 2012; Young and Borch, 2012). A group of organic micropollutants that have recently come into focus comprises the perfluoroalkyl and polyfluoroalkyl substances (PFASs), which seem to be developing into a problem contaminant around the world (Kotthoff et al., 2015; Simon, 2014). Organic micropollutants have been detected in all parts of the hydrological cycle, including rainwater (Fernández-González et al., 2014; Guidotti et al., 2000), surface waters (e.g., Bu et al., 2015; Buser et al., 1998; Gros et al., 2007; Kolpin et al., 2002; Loos et al., 2009; Ternes, 1998), groundwater (e.g., Barnes et al., 2008; Halling-Sorensen et al., 1998; Lapworth et al., 2012; Loos et al., 2010), and drinking water (e.g., Heberer, 2002; Kunacheva et al., 2010; Leal et al., 2010; Post et al., 2012; Stackelberg et al., 2004; Stan and Heberer, 1997).
The main pathway into aquatic environments in rivers or lakes is through discharge from sewage treatment plants (Gros et al., 2007; Heberer et al., 2002; Jekel et al., 2015; Karthikeyan and Meyer, 2006; Metcalfe et al., 2003; Paxeus, 2004; Rabiet et al., 2006; Stackelberg et al., 2004). Organic micropollutants can also enter aquatic environments from livestock farming (Aga et al., 2003), landfill sites (Albaiges et al., 1986; Barnes et al., 2004; Holm et al., 1995), wastewater irrigation of fields (Scheytt et al., 1998; Ternes et al., 2007), leaking sewers (Fenz et al., 2005b; Gallert et al., 2005; Phillips et al., 2015), and on-site water treatment units and septic systems (Carrara et al., 2008; Godfrey et al., 2007). Although techniques exist (and are technically possible) for removing organic micropollutants during wastewater treatment (e.g., activated carbon adsorption, advanced oxidation processes, nanofiltration, reverse osmosis, and membrane bioreactors) standard wastewater treatment plants (WWTPs) do not usually completely remove all organic micropollutants (Evgenidou et al., 2015; Kreuzinger, 2008; Luo et al., 2014; Scheurer et al., 2010; Suárez et al., 2008; Tijani et al., 2013; Vona et al., 2015). This is largely due to the broad variety of organic compounds, each requiring different removal techniques due to their different chemical properties. Moreover, metabolites can form during WWTP processes (e.g., Boix et al., 2016; Evgenidou et al., 2015; Göbel et al., 2005). Even irradiation with UV light (Bergheim et al., 2015) or ozonation (Favier et al., 2015) can lead to the formation of metabolites with higher ecotoxicity than their parent compounds. Accordingly, this implies that organic micropollutants are still continuously released into the aquatic environment and concentrations in aquatic environments are increasing rather than decreasing. This is a reason for great concern as both aquatic fauna and humans are, to a greater or lesser extent, exposed to water that contains organic micropollutants. Adverse health effects on fish (e.g., reproductive and cytological effects) are frequently reported (Brooks, 2014; Overturf et al., 2015; Triebskorn et al., 2007). Numerous investigations have also been published into human health risks resulting from organic micropollutants in water (e.g., Rahman et al., 2009; Schwab et al., 2005; Stuart et al., 2012).
The number of investigations into organic micropollutants has increased in
line with continuing improvements in analytical techniques, such as the use
of mass spectrometry (and enhancements) and solid-phase extraction methods.
These organic compounds can now be quantified down to low ng L
In view of the above-mentioned concerns regarding the presence of organic micropollutants in aquatic environments, and especially in groundwater, there is a clear need to develop a sound understanding of how they are transported and behave in groundwater. Laboratory experiments on the transport and eventual fate of organic micropollutants under defined boundary conditions will always be important because the boundary conditions for field studies are poorly known, which affects the transferability of their results to other systems. This paper therefore provides a review of published column experiments investigating the properties and transport behavior of organic micropollutants, since such experiments provide a suitable setup for this task. The relevant transport properties of organic micropollutants are first presented, followed by a discussion of which compounds and which of their properties can be investigated using the experimental setup of a column experiment. The weaknesses and problems, as well as the advantages, of different experimental setups will be discussed and, finally, other laboratory methods that can be used to investigate organic micropollutants are compared with column experiments.
The transport of (organic) solutes in groundwater depends on the chemical and physical properties of the compounds, the solvent (i.e., the groundwater, including all solutes), and the substrate (the aquifer material). The main processes of solute transport are advection and hydrodynamic dispersion. The movement of solutes can be retarded compared to that of the containing groundwater, mainly as a result of sorption. Biodegradation and chemical reactions, resulting in oxidation-reduction reactions, precipitation–dissolution reactions, and mechanical filtering, are other mechanisms that can also reduce the velocity of solutes during their transport in groundwater, which is only driven by advection and hydrodynamic dispersion. Substances that behave in a very similar way to groundwater are known as conservative tracers. Such substances (e.g., bromide, chloride, uranine, eosine) are used, not only in laboratory experiments, but also in field tracer tests, to identify differences between the transport behavior of reactive substances and the groundwater movement.
The effect that different factors have on solute transport in groundwater is shown as a schematic breakthrough curve in Fig. 1. A conservative tracer or substance does not react with the soil and/or groundwater, nor does it undergo biological or radioactive decay (Fetter, 1988). It is only influenced by advection and hydrodynamic dispersion (the red breakthrough curve in Fig. 1). In contrast, a non-conservative tracer or substance is likely to react with the soil and/or groundwater and its transport will be influenced by many (or all) of the controlling factors for solute transport described below.
Schematic representation of solute transport in groundwater, taking
into account the main transport processes of advection, hydrodynamic
dispersion, retardation, and degradation. Breakthrough curves (BTCs) were modeled using the CXTFIT code (Toride et al.,
1999). The model was set up as a deterministic equilibrium CDE with
flux-averaged concentration and dimensionless parameters. The characteristic
length was set to 100. The initial values are
The most important factor influencing solute transport in groundwater or during a column experiment is advection, which involves the transport of dissolved or suspended matter with water. In Fig. 1 the gray rectangle with black solid borders represents the start distribution of a given substance or compound and its area represents the mass of the substance or compound (i.e., its concentration times the distance). If advection is the only factor affecting the transport of this substance within the groundwater or column, the rectangle will move along the flow path without any alteration (as indicated by the gray rectangle with gray borders on the right-hand side of Fig. 1).
Diffusion results in the equalization of concentration differences, following Fick's law. Diffusion is stronger at low flow velocities and small scales and becomes almost insignificant as the velocities and scale increase. The impact of diffusion within an aquifer is therefore often negligible. However, diffusion can be significant if a column experiment is operated at low flow velocities or at a very small scale.
Mechanical dispersion results in a spreading of the concentration front of a solute during transport and is caused by velocity and flow path variations within an aquifer due to inhomogeneities on both micro and macro scales (Domenico and Schwartz, 1998). It results in both transverse (or lateral) dispersion (i.e., a spreading of the solute perpendicular to the flow direction) and longitudinal dispersion (differences in travel time; Appelo and Postma, 2005). The impact of dispersion on mass transport in groundwater increases with velocity and scale.
The combination of diffusion and mechanical dispersion is known as hydrodynamic dispersion; it leads to a broadening of the breakthrough curve at a particular observation point during flow through a porous medium (the red curve in Fig. 1).
Retardation is the delay in the transport of a substance relative to the groundwater flow. There are many possible reasons for the retardation of a reactive solute, but the main mechanism involved is sorption, which involves reactions between solutes and solid surfaces (Domenico and Schwartz, 1998). Sorption can occur either by adsorption onto surfaces or by absorption into the substrate. The opposite process is desorption. Sorption and desorption of various species often occur as coupled processes in which dissolved ions of one species displace sorbed ions of another species from their sorption sites in the substrate. These processes are dependent on combinations of various specific properties of the compounds, such as their charge, the size of the ions, their concentration in the solution, and the availability of sorption sites. Combined sorption–desorption processes are known as ion exchange. The increase in solute concentration during a breakthrough typically leads to changes in the equilibrium between the solid and liquid phases of the compound. Under sufficiently low flow rates, equilibrium conditions between the solid and fluid phases will be established. Then, a compound can only break through when all sorption places are filled according to the new equilibrium. When the system is flushed with compound-free water the opposite process takes place, the equilibrium shifts back, and the sorbed compounds are again released into the solution. The result is a delayed breakthrough curve at the observation point (blue curve in Fig. 1). However, under high flow regimes within the column, non-equilibrium conditions might prevail, which can significantly affect the described processes. Typical retardation values range between 1.2 and 10 (Langguth and Voigt, 2004). Since it is difficult to observe sorption–desorption processes directly, either in the field or in a laboratory, the retardation of a breakthrough curve is commonly used as an indicator of these processes.
Schaffer and Licha (2015) described the two main sorption processes as
(i) hydrophobic sorption and (ii) ionic sorption. Hydrophobic sorption occurs
during the transport of non-polar, non-ionized (neutral) compounds, which
sorb onto the uncharged sites of organic matter. Neutral compounds often show
a degree of hydrophobic behavior due to the polarity of the water molecules
and they therefore have a tendency to accumulate in non-polar environments
(solid or liquid organic phases). Organic matter is one of the best sorbents
for non-polar organic compounds because of its large specific surface (Delle
Site, 2001). If a soil contains more than 0.1 % organic carbon the
adsorption of non-ionic organic substances is attributed entirely to the
organic carbon (Appelo and Postma, 2005). Ionic sorption occurs during the
transport of polar, ionized (anionic or cationic) compounds. Non-neutral
compounds often show more hydrophilic behavior and are therefore often more
conservative than neutral compounds. However, in the presence of charged
sorption sites (clay minerals, organic matter, Fe
The species of dissociative organic compounds in groundwater is controlled by
the dissociation constant of organic acids,
The pH of groundwater therefore defines the polar character of organic compounds and consequently has an important influence on the sorption behavior of the compounds (Schwarzenbach et al., 2003).
The distribution coefficient (
In order to obtain the distribution coefficient between a solid and liquid
phase (
If the distribution coefficient between the solid and liquid phases
(
Degradation is the mineralization of complex molecules to form inorganic
molecules or elements. A reactive substance can undergo chemical, biological,
or radioactive degradation during transport, which will reduce both the
concentration and the total content of that particular substance (Fetter,
1988; Fig. 1), and hence lower the breakthrough curve (green curve in
Fig. 1). Radioactive decay is not relevant for organic compounds, but both
microbial degradation and chemical degradation (e.g., hydrolysis,
oxidation-reduction reactions, UV degradation) are potentially significant.
Redox reactions are electron transfer reactions that induce metabolism of
organic molecules. This can lead to total mineralization and to the formation
of metabolites with properties that may differ from those of the original
molecules. In most cases these processes are catalyzed by microbes
(Schwarzenbach et al., 2003). Redox reactions during groundwater transport as
a result of changes in thermodynamic conditions along the flow path can also
affect the solubility of a compound. Redox processes control the natural
concentrations of O
Organic compounds include a very wide range of the compound-specific
properties described above. Different species and metabolites of a compound
can exist in the same sample. Transport behavior is furthermore dependent on
the pH of the fluid and on the properties of the substrate (e.g., the organic
carbon content, the presence of sorption sites on clay minerals, or the
presence of Fe
Laboratory column experiments can be used for many different applications. The boundary conditions and experimental setup can be varied to best address particular research questions or compounds. Column experiments are generally used to investigate the transport behavior, sorption, and degradation of a specific compound or group of substances. However, more specific issues can also be addressed, such as the effect of entrapped air on soil permeability (Christiansen, 1944), the effect of a fluctuating water table on redox conditions (Sinke et al., 1998), the effect of preferential flow on solute transport (Schoen et al., 1999), the effect of a sterilized soil on sorption (Lotrario et al., 1995), or the influence of methanol on the retention of hydrophobic organic chemicals (Nay et al., 1999). An overview of the types of investigations that can be carried out with column experiments is provided below, the methods involved presented, and practical issues discussed. Additional information on column experiments for specific compounds is provided in Sect. 3.2.
The basic principle of a column experiment is to pump water with a specific composition, including solutes of interest, through a column filled with a specific substrate. Since the aim of column experiments is to investigate (dynamic) processes it is common to start with a “neutral” fluid that is in hydrochemical equilibrium with the substrate, and then switch to the actual test fluid containing the solutes of interest and a mandatory conservative tracer. Figure 2 shows three basic setups for laboratory column experiments. The normal practice is to operate columns in an upright position. This allows percolation through the unsaturated zone to be simulated, as shown in Fig. 2a. Different degrees of saturation can thus be achieved within the column and the effects of a fluctuating water table can also be simulated. In order to simulate saturated groundwater conditions, the column can be operated with an upward flow direction, as shown in Fig. 2b. This results in saturated conditions throughout the column and may help to avoid any entrapment of gas bubbles. Different parameters or boundary conditions and their evolution along a flow path can be investigated by coupling several columns together, as shown in Fig. 2c.
Schema of common setups used in column experiments:
Columns are typically made of stainless steel (Alotaibi et al., 2015; Banzhaf et al., 2012; Burke et al., 2013; Schaffer et al., 2015; Unold et al., 2009; Xu et al., 2010) in order to prevent interactions with solutes, or from acrylic glass (Gruenheid et al., 2008; Hebig et al., 2016; Rauch-Williams et al., 2010; Yao et al., 2012) for improved visual control of saturation levels and tracer transport (where a dye tracer is used). Although acrylic glass is a synthetic plastic, it is reported to be inert to many organic solutes (Hebig et al., 2014) and therefore suitable for column experiments with organic micropollutants. Glass columns are also often used (e.g., Estrella et al., 1993; Fan et al., 2011; Nay et al., 1999; Persson et al., 2008; Simon et al., 2000) since glass is assumed to be inert with respect to organic compounds. Other materials used for columns include PVC (e.g., Bertelkamp et al., 2012; Greenhagen et al., 2014; Salvia et al., 2014; Sinke et al., 1998), polyethylene (Bertelkamp et al., 2012), and aluminum (Burke et al., 2014).
The physical dimensions of columns are not yet standardized. Short columns enable fast experiments and many repetitions, while longer columns need more time for equilibration before the actual start of the experiment but allow longer reaction times (which means that more reactions can be distinguished) and more complex settings (e.g., the establishment of redox zonings). The dimensions of columns vary considerably due to the broad range of research applications; for example, lengths have been reported ranging from 5 cm (Estrella et al., 1993; Teijón et al., 2014) to 2.4 m (Cordy et al., 2004), and inner diameters have been reported ranging from 2 cm (Teijón et al., 2014) to 36 cm (Bertelkamp et al., 2012, 2014). The choice of a reasonable length-to-diameter ratio is important if scaling effects are to be avoided. In long columns with small diameters the main flow (and hence the main transport) can occur by preferential flow along the boundary between the inner column surface and the sediment grains. If the diameter is too large, transversal dispersivity can become significant for solute transport, which makes analysis even more complex (column experiments are generally designed to exclude transversal dispersivity from the transport model). Using a short column with too large a diameter relative to its length may prevent uniform, homogeneous flow within the sediment. Lewis and Sjöstrom (2010), in their extensive review of the design of column experiments, recommended a diameter-to-length ratio of 1 : 4 in order to avoid such effects. However, this ratio is rarely reported in the published literature.
The tubing and other materials reported are commonly made from Teflon/PTFE (e.g., De Wilde et al., 2009; Fan et al., 2011; Ke et al., 2012; Strauss et al., 2011; Teijón et al., 2014) or stainless steel (e.g., Ke et al., 2012; Nay et al., 1999; Teijón et al., 2014), which are known to be inert materials. Other materials that have been used in column experiments are Pharmed tubing (Strauss et al., 2011), (dark) polyethylene (Bertelkamp et al., 2012, 2014), PVC, (black) Tygon tubing, vinyl, polysulfone, brass (Greenhagen et al., 2014), polypropylene, and silicone (Srivastava et al., 2009). However, the influence of many of these reported materials on organic compounds is unknown and may be problematic, as is the case for Pharmed tubing, silicone, and Tygon tubing (Hebig et al., 2014). Unfortunately, there is often little information provided concerning the materials used (in particular the filter materials used), even though they can have a significant influence on the mass recoveries of organic micropollutants. Only a few investigations have specifically addressed the issue of possible interactions between the materials used in experiments and the compounds and fluids under investigation, or included preliminary investigations to allow such interactions to be avoided (Greenhagen et al., 2014; Gruenheid et al., 2008; Hebig et al., 2014; Srivastava et al., 2009). This often untested impact of laboratory materials on compound concentrations may therefore have a significant (but unknown) influence on the results of column experiments.
Peristaltic pumps are commonly used in investigations into fluid transport in saturated columns (e.g., Banzhaf et al., 2012; Bertelkamp et al., 2014; De Wilde et al., 2009; Müller et al., 2013; Rodriguez-Cruz et al., 2007) because they are able to provide uniform flow, even at low flow rates. Other pumps that have been used include pulsating pumps (Massmann et al., 2008), piston pumps (Persson et al., 2008), gear pumps (Alotaibi et al., 2015) and, in unsaturated and leaching experiments, suction pumps (Salem Attia et al., 2013; Siemens et al., 2010). In leaching experiments it is also common to simply let the fluid leach under gravity (Scheytt et al., 2006, 2007; Xu et al., 2010).
To prevent inhomogeneous flow and mass transport through the column, a layer of clean, well-sorted filter quartz sand is often included at both the inlet and outlet ends of the column (e.g., Banzhaf et al., 2012; Gruenheid et al., 2008; Persson et al., 2008; Unold et al., 2009). Other materials reported to have been used as filters are glass beads or globes (e.g., Salvia et al., 2014; Scheytt et al., 2004, 2006) and glass wool (Nay et al., 1999; Salem Attia et al., 2013). These filter layers are often combined with manufactured filters such as perforated PVC (Bertelkamp et al., 2014) or stainless steel plates (Strauss et al., 2011), stainless steel meshes (Burke et al., 2013; Fan et al., 2011; Patterson et al., 2010; Yao et al., 2012), glass fiber filters (Strauss et al., 2011), cheesecloths (Fan et al., 2011; Srivastava et al., 2009), aluminum screens (Greenhagen et al., 2014), porous HDPE (Lorphensri et al., 2007), Teflon gauze nets (Scheytt et al., 2004; T. Scheytt personal communication, 2016), porous glass (Siemens et al., 2010), porous ceramic plates (Unold et al., 2009), or paper filters (Srivastava et al., 2009). In this way the hydraulic contrast between the tube/inlet and the substrate can be reduced and the incoming water and solutes spread over the entire width of the column. Any intrusion or wash-out of finer sand particles through the inlet or outlet of the column should also be avoided through the use of such filters.
A wide range of substrates have been used as filling for columns in published
research, depending on the specific objectives. These include natural
(site-specific) aquifer sediment (e.g., Alotaibi et al., 2015; Burke et al.,
2013; Lopez-Blanco et al., 2005; Mersmann et al., 2002; Preuss et al., 2001;
Teijón et al., 2014), natural soil (e.g., Aga et al., 2003; Cordy et al.,
2004; Kamra et al., 2001; Murillo-Torres et al., 2012; Rodriguez-Cruz et al.,
2007; Xu et al., 2010), artificial soil (De Wilde et al., 2009), artificial
(model) sediments such as well-sorted filter sands or technical quartz sands
(e.g., Baumgarten et al., 2011; Bertelkamp et al., 2014; Greenhagen et al.,
2014; Nay et al., 1999), and other artificial materials such as iron-coated
sand (Hebig et al., 2016), magnetic nanoparticle-coated zeolite (Salem Attia
et al., 2013), alumina, silica gel (Lorphensri et al., 2007), and biochar
(Yao et al., 2012). The substrate can be installed wet or saturated (e.g.,
Alotaibi et al., 2015; Nay et al., 1999; Simon et al., 2000), or dry (e.g.,
Banzhaf et al., 2012; Fan et al., 2011; Scheytt et al., 2007; Sinke et al.,
1998; Teijón et al., 2014), ideally in small (1–2 cm) layers that are
individually compacted using a tool such as a stamp (Banzhaf et al., 2012),
plunger (Scheytt et al., 2007), or pestle (Unold et al., 2009), or
alternatively by vibration (Burke et al., 2013; Teijón et al., 2014), by
tapping against the column (Bertelkamp et al., 2014; Strauss et al., 2011),
or by placing a weight on top before installing the next layer (De Wilde et
al., 2009). Some experiments have been performed using undisturbed soils or
sediments (e.g., Greenhagen et al., 2014; Massmann et al., 2008;
Muñoz-Leoz et al., 2011). However, even when great care is taken over
filling the column, the resulting effective porosities are usually higher
than in natural sediments. The reported range of effective porosities is
between 0.28 (Greenhagen et al., 2014) and 0.49 (Schaffer et al., 2015), of
which only the lower limit is representative of effective porosities found in
natural aquifers. It appears that effective porosities lower than 0.30 are
only achieved when the column is filled with undisturbed sediments. The high
effective porosities in most experiments may lead to lower flow velocities
and lower reactive surface areas than would be expected in a natural
environment and may therefore be responsible for the often noted differences
between laboratory results and results from field tests. However, many
published investigations either do not specify the effective porosities or
only report the total porosities (from the ratio of the weight of the column
filled with dry sediment to the weight of the column with fully saturated
sediment). Porewater velocities vary according to the experimental
conditions. The flow velocity should ideally reproduce natural groundwater
flow velocities, which one would normally expect to be between
1 cm d
The reported solute concentrations vary considerably depending on the
objectives of the individual experiments, ranging between 60 ng L
An important basic parameter is the pH of the fluid used in column
experiments as the polar character of many organic micropollutants varies
according to the relationship between the
Field parameters and/or tracers are commonly measured using flowthrough cells fitted with probes (e.g., Mersmann et al., 2002; Müller et al., 2013). Sampling for solutes can be performed in a number of different ways, including “by hand” (bottle, beaker, e.g., Burke et al., 2014; Cordy et al., 2004; Hebig et al., 2016), using an automated fraction collector (e.g., Banzhaf et al., 2012; Rodriguez-Cruz et al., 2007; Scheytt et al., 2004; Srivastava et al., 2009; Unold et al., 2009), using sampling ports attached laterally to the column (Alotaibi et al., 2015; Baumgarten et al., 2011; Burke et al., 2014), or online and in real time using a spectrometer at the outlet from the column (Teijón et al., 2014). Sampling ports alongside the column risk altering the hydraulics within the column and should only be used with great caution, using small volumes and low sampling rates. A further means of assessing the fate of organic compounds in a column experiment is to extract the substrate from the column after completing the experiment and to analyze the solid phase for irreversibly sorbed solutes (Banzhaf et al., 2012). This enables all parts of the mass balance to be determined since the mass of solute degraded can be determined by deducting the recovered mass and the irreversibly sorbed mass from the injected mass.
The boundary conditions of column experiments need to be known in order to ensure correct interpretation of the results obtained. Van Genuchten and Parker (1984) discussed the physical and mathematical significance of the boundary conditions that apply to 1-D solute transport in laboratory column experiments. They presented solutions of the convective–dispersive transport equation that can be used to analyze column effluent data. Leij et al. (1993) investigated analytical solutions for non-equilibrium solute transport in 3-D porous media. They found that the effect of non-equilibrium on 1-D transport is similar to that on 3-D transport. The effect of non-uniform boundary conditions on steady flow in saturated homogeneous cylindrical soil columns was investigated by Barry (2009). He reported that uniform flow in the column could be achieved if a baffle zone was established at each end of the column. Sentenac et al. (2001) used fiber-optic sensing to measure side-wall boundary effects in soil columns. They detected flow velocity differences between the center of the column and the boundary wall, whose surface roughness was varied. Seyfried and Rao (1987) described preferential flow effects in columns containing undisturbed substrates. They assumed that flow occurred through series of large pores or pore sequences. Perret et al. (2000) investigated preferential solute flow in columns containing undisturbed substrates using a tomography technique derived from medical applications. This technique allowed real-time analysis of the flow patterns of radioactive tracers in both 2-D and 3-D.
The dimensions of a column used for in-column experiments appear to have a marked influence on the hydraulic conditions within the column and on the transport of the investigated compounds (see Sects. 2 and 3.1.1). Wierenga and Van Genuchten (1989) found that the dispersivity of chloride and tritium tracers used in unsaturated column experiments increased significantly with column length, while retardation factors remained essentially the same. However, Ribeiro et al. (2011) showed that the retardation factor for potassium ions investigated during leaching experiments decreased with increasing column length, and that the dispersive–diffusive coefficient and dispersivity both increased with increasing porewater velocity and increasing soil column length. Rühle et al. (2013) described changes over time to the water flow through the porous medium in a column, from uniform to non-uniform. They concluded that flow path changes occurred due to clogging of small pores near the column inlet as a result of microbial growth and calcite precipitation, which then caused non-uniform water flow and solute transport. Bromly et al. (2007) reviewed published data on experiments on almost 300 repacked saturated homogeneous column experiments. They related the dispersivity to the length of the column used; i.e., short columns had greater dispersivities. However, clay content was identified as the most important factor controlling dispersivity, followed by the diameter of the column. However, they pointed out that the individual experimental design (e.g., the column geometry, inlet dead volume, and soil packing) needs to be taken into account in order to be able to relate dispersivity to soil properties. Although not specifically addressing column experiments, Schulze-Makuch (2005) provided a useful overview of longitudinal dispersivity data for different materials and implications for scaling behavior. Nimmo and Akstin (1988) investigated the hydraulic conductivity of a sandy soil with a low water content following compaction by various methods and found that conductivities varied by almost 3 orders of magnitude, depending on the compaction.
It should be pointed out that the main issues when evaluating and
interpreting results from column experiments are the same for different
organic compounds, these being the interactions with surfaces and the ionic
or neutral character of the molecules. In order to describe the transport
behavior of organic solutes in column experiments, their (reactive)
breakthrough curves are often compared with the breakthrough curves of a
conservative tracer (which allow inferring the flow velocity of the fluid).
This is sometimes performed by graphical analysis (e.g., Scheytt et al.,
2004). In both reactive and conservative breakthrough curves the instant is
identified at which the observed concentration reaches 50 % of the
injected concentration (i.e.,
Time (or temporal) moment analysis has also frequently been used to evaluate
column experiments (Kamra et al., 2001; Murillo-Torres et al., 2012). This
method allows the mean breakthrough time, spreading, and asymmetry of a
breakthrough curve to be characterized by integration of the breakthrough
curve (Appelo and Postma, 2005; Hebig et al., 2015). It is, however, more
common to model (or “fit”) the solute concentrations to the
convective–dispersive equation (CDE):
The computer software commonly used to fit the CDE to observed breakthrough curves is the CXTFIT software (Toride et al., 1999), which is available in various forms, for example through the public domain STANMOD software (Šimůnek et al., 1999), and can be downloaded at no cost (e.g., Bertelkamp et al., 2014; Fan et al., 2011; Kamra et al., 2001; Müller et al., 2013; Persson et al., 2008; Schaffer et al., 2015; Unold et al., 2009). The free HYDRUS-1D software (Šimůnek et al., 2009) is also frequently used for transport parameter determination (De Wilde et al., 2009; Strauss et al., 2011; Teijón et al., 2014). Other relevant available software packages are Origin (Microcal, 1995), as used for example by Alotaibi et al. (2015), PHREEQC (Appelo and Postma, 2005), as used for example by Burke et al. (2013), PEST (Watermark Numerical Computing, 2005), as used for example by Burke et al. (2014), and libSRES (Ji and Xu, 2006), as used for example by Fan et al. (2011).
Column experiments on different groups of organic micropollutants are described in greater detail below, distinguishing between investigations into pharmaceuticals, pesticides, and other organic micropollutants. Selected column experiments for non-organic compounds are also presented for comparison. The presented examples of column experiments are then summarized and discussed.
Column experiments are often used to investigate the transport of pharmaceutical compounds under both saturated and unsaturated conditions. These are presented separately in this section, beginning with experiments under saturated conditions.
Mersmann et al. (2002) investigated the transport of carbamazepine, clofibric
acid, diclofenac, ibuprofen, and propyphenazone under saturated conditions
and found the transport of all of these compounds to be unaffected by changes
in the pH, temperature, dissolved oxygen content, or ion concentration of the
water used. Moreover, they found that each of these compounds except for
clofibric acid was retarded in the column. In contrast, Gruenheid et
al. (2008) found that temperature affected the degradation of
sulfamethoxazole under saturated conditions, with higher temperatures
resulting in increased mineralization. However, the biodegradation of
iopromide was high at all investigated temperatures (5, 15, and
25
Unold et al. (2009) investigated sulfadiazine under near-saturated conditions and found the degradation to be light-dependent. Furthermore, sulfadiazine was sorbed onto the upper layer of the investigated soil column, despite showing a high leaching potential. Oppel et al. (2004) investigated the leaching of pharmaceuticals in unsaturated soil columns. They found a low leaching potential for diazepam, ibuprofen, ivermectin, and carbamazepine, while clofibric acid and iopromide were highly mobile. Siemens et al. (2010) also carried out leaching experiments under unsaturated conditions with naproxen, ibuprofen, bezafibrate, diclofenac, gemfibrozil, clarithromycin, trimethoprim, clindamycin, erythromycin, and metoprolol; they found that the investigated clay soil had significant potential to retain these pharmaceuticals. The retention capacity was, however, limited, and all compounds were leached to some extent. Wu et al. (2010) found low mobility for carbamazepine, diphenhydramine, fluoxetine, diltiazem, and clindamycin, and also for two metabolites (carbamazepine-10,11-epoxide, and norfluoxetine), in unsaturated leaching experiments. Moreover, carbamazepine, diphenhydramine, and fluoxetine were persistent throughout the experiment. Leaching experiments carried out by Salvia et al. (2014) indicated that the transfer and degradation of the investigated pharmaceuticals, these being sulfonamides (sulfanilamide, sulfadiazine, sulfathiazole, sulfameter, sulfadimidine, sulfabenzamide, sulfadimethoxine, and sulfamethoxazole), macrolides (erythromycin, tylosin, and roxithromycin), trimethoprim, dicyclanil, penicillin G, carbamazepine, fluvoxamine, and paracetamol, were dependent on the soil characteristics, i.e., on the amount of clay in the soil and its pH. All of the investigated compounds were found to degrade both substantially and rapidly except for roxithromycin and carbamazepine, which were relatively persistent. Kay et al. (2005) investigated the leaching of oxytetracycline, sulfachloropyridazine, and tylosin from clay soils after slurry application. Although the pH was significantly affected by the slurry, this had no effect on oxytetracycline leaching. Scheytt et al. (2006) found that diclofenac, ibuprofen, and propyphenazone showed similar mobilities in both saturated and unsaturated column experiments, but carbamazepine showed lower sorption and elimination under unsaturated conditions than under saturated conditions.
In contrast to column experiments on pharmaceuticals, most column experiments on pesticide leaching have been carried out under unsaturated conditions in order to reflect the main input path of pesticides into groundwater, which is through agricultural use.
Nkedi-Kizza et al. (1987) carried out leaching experiments on atrazine and diuron using various mixtures of water and methanol. They found a significant reduction in the retardation factor as the volumetric fraction of the organic cosolvent methanol increased. Persson et al. (2008) reported leaching of 30 % of the investigated chlorophenols from contaminated soils, of which 1–3 % was associated with colloids. Increasing the porewater velocity had no influence on their mobility. Lopez-Blanco et al. (2005) investigated the transport of endosulfan under unsaturated conditions. They found that high soil moisture favored the transport of this compound by forming and maintaining preferential flow paths in the soil. Rodriguez-Cruz et al. (2007) compared the leaching and retention of linuron, atrazine, and metalaxyl from clayey soils, with and without cationic surfactant treatment. They found that linuron was immobilized in treated soil and that the leaching of atrazine and metalaxyl was reduced. De Wilde et al. (2009) carried out sorption and degradation experiments on pesticides under unsaturated conditions. The mobility of the investigated pesticides was ranked on the basis of their results as bentazone > metalaxyl > isoproturon > linuron, and their degradability as linuron > metalaxyl > isoproturon > bentazone. Bertelkamp et al. (2014) carried out column experiments on atrazine under saturated conditions and found it to be persistent.
Although not the primary subject of this review, column experiments are also used to investigate non-organic compounds and selected investigations are presented in this section. Smith et al. (1985) investigated the transport of Escherichia coli through both disturbed and undisturbed soil columns. They found mixed and repacked soils to be much more effective in filtering the bacteria than undisturbed soils, which allowed up to 96 % of the Escherichia coli to pass through the columns. Jin et al. (2000) investigated virus removal and transport in both saturated and unsaturated sand columns and found significantly higher removal under unsaturated flow conditions. Pang et al. (2002) investigated the effect of porewater velocity on the transport of Cd, Zn, and Pb under non-equilibrium chemical conditions in alluvial gravel columns; they found the proportion of exchange sites available to be independent of the porewater velocity. Amos et al. (2004) investigated the remediation of acidic mine drainage using column experiments and found Fe removal during long-term operation of column experiments to be a good indicator of the column's ability to remediate acidic mine drainage. Ilg et al. (2007) investigated colloid transport in unsaturated soil columns. They found that colloid transport could be overestimated, depending on the sampling system used.
As described and discussed in Sects. 3.1 and 3.2, laboratory column experiments are suitable (and widely used) for investigations into the fate of organic micropollutants. There are, however, alternative methods available that can also be used for this purpose, depending on the objectives and the facilities available. Incubation experiments (such as batch experiments and microcosms) used, for example, to determine sorption coefficients (Scheytt et al., 2005) or to investigate the persistence of pharmaceuticals (Lam et al., 2004), the elimination of pharmaceuticals (Radke and Maier, 2014), the microbial degradation potential of pollutants (Barra Caracciolo et al., 2013), or the biotransformation of micro-contaminants (Nödler et al., 2014).
By far the largest number of comparisons have been made between batch experiments and column experiments on organic micropollutants, and we therefore focus on comparisons between these two laboratory methods. However, selected alternative methods are also discussed briefly in this section.
Maeng et al. (2011) investigated the biodegradation of pharmaceuticals in batch and column experiments. More specifically, the batch experiments were used to investigate removal of the pharmaceuticals and the column experiments were then used to differentiate between biodegradation and sorption of the compounds. Murillo-Torres et al. (2012) used batch and column experiments to investigate the sorption and mobility of organic micropollutants. They obtained contradictory results from the different methods concerning the mobility of di-2-ethyl(hexyl)phthalate and 4-nonylphenol and suggested that the higher mobility of di-2-ethyl(hexyl)phthalate in the column experiments could be due to the formation of complexes within the soil. Salem Attia et al. (2013) used batch and column experiments to investigate the adsorption of pharmaceuticals to nanoparticles. The batch experiments were used to investigate the influence that the contact time, pH, and concentrations of the compounds had on adsorption and the column experiments were used to investigate the removal efficiency of the nanoparticles. Simon et al. (2000) investigated the influence of redox zonation on the transformation of p-cyanonitrobenzene. They observed a reduction of the compound during column experiments that was an order of magnitude faster than predicted from the results of previously conducted batch experiments. De Wilde et al. (2009) investigated the sorption and degradation of isoproturon, bentazone, metalaxyl, and linuron. The distribution coefficients that they fitted to the column experiments were much smaller than those obtained from previous batch experiments.
A number of investigations have also combined batch and column experiments
in order to optimize their results, for example when investigating the
degradation of sulfamethoxazole (Baumgarten et al., 2011), the transport of
benzotriazole and its sorption to zerovalent iron (Jia et al., 2007), or the
influence of ozonation on the formation and removal of carbamazepine and its
degradation products (Hübner et al., 2013). Ke et al. (2012) used column
and batch experiments to investigate the sorption and biotransformation of
six endocrine-disrupting compounds (estrone, 17
Benker et al. (1998) compared the retardation coefficients for
trichloroethene estimated from batch and column experiments with field data.
They were able to confirm the sorption behavior observed in column
experiments from the field data and, provided the sorptive properties of the
sediment were correctly determined, batch experiments then allowed the
retardation of trichloroethene in the field to be reliably predicted. Scheytt
et al. (2007) compared the results obtained from unsaturated column
experiments on clofibric acid, diclofenac, ibuprofen, and propyphenazone to
field measurements from former sewage farms. They were thus able to confirm
the transport behavior observed in the laboratory experiments through their
own field measurements. Bertelkamp et al. (2012) also compared the
degradation of organic micropollutants in column experiments with field
measurement. Results from laboratory experiments and from investigations at a
riverbank filtration site both showed that the charge and Log
Batch experiments (i.e., incubation experiments in general) can therefore be a useful laboratory method to combine with column experiments in order to characterize the transport behavior of organic micropollutants. Batch experiments can yield reasonable retardation predictions for organic micropollutant compounds. However, sorption coefficients obtained from batch experiments are often not suitable for determining solute transport, either in column experiments or in the field (e.g., De Wilde et al., 2009). This is because in batch experiments sorption is determined under equilibrium conditions, whereas column experiments determine sorption under non-equilibrium conditions, or at least under dynamic conditions. Sorption also occurs much more rapidly under batch conditions than under flow conditions, which may be due to vibration and a high solution-to-soil ratio (Kookana et al., 1992).
The equilibrium established within a column may differ from that in the inflowing water during the experiment. Batch experiments are often carried out using unrealistic sediment-to-water ratios (e.g., 1 : 5) that do not reflect realistic aquifer conditions. While the theoretical maximum sorption capacity (under ideal conditions and equilibrium) can be reasonably well determined from batch experiments, neither advection nor the (dynamic) sorption–desorption behavior (for example) can be determined with this method. Batch experiments are therefore an established method and are suitable for determining equilibrium parameters for interactions between a specific organic compound and a specific sediment, but they are less able to reproduce the dynamic (i.e., non-equilibrium) groundwater conditions of an aquifer than column experiments. Batch sorption experiments are therefore suitable for determining the sorption behavior of specific compound–sediment combinations under equilibrium conditions, while column experiments are more suitable for determining the transport behavior of specific compound–fluid–sediment combinations, under either equilibrium or non-equilibrium conditions.
The practical objectives of column experiments on organic micropollutants relate to possibilities for their removal, either by natural processes during passage through soil and groundwater, or by technical processes such as those used in WWTPs. They can also improve our understanding of the relationship between the properties of specific compounds and the properties of fluids and aquifers, with the objective of using organic micropollutants as indicators of aquifer conditions and groundwater history. Different boundary conditions (such as the redox conditions and the degree of saturation) clearly have a strong influence on the transport and degradation of organic micropollutants, and are therefore critical to defining the transport behavior of a specific organic compound in a way that is applicable to any given hydrogeochemical environment. Unfortunately the results of most column experiments therefore remain restricted to the specific boundary conditions of each column experiment, since variations in just one of these boundary conditions (e.g., the redox conditions) can have a major impact on the results. For example, the degradation of a compound such as sulfamethoxazole can range from rather low levels (e.g., Suarez et al., 2010) to high levels (e.g., Banzhaf et al., 2012), depending mainly on the redox conditions. The degradation of sulfamethoxazole has been shown to be strongly dependent on the organic carbon content of the sediment (Hebig et al., 2016). The behavior of such compounds can also depend on the level of saturation. Diclofenac, for example, shows high levels of degradation under unsaturated conditions but very low levels of degradation under saturated conditions (Scheytt et al., 2004, 2007).
Another problem faced in designing technical processes to remove organic compounds is that different organic compounds can be sensitive to different hydrochemical conditions, which makes it difficult to find conditions that will allow all such compounds to be removed at the same time. However, column experiments can assist in finding such conditions, as it is rather easy with this experimental setup to vary the boundary conditions. Batch sorption experiments are suitable if only the equilibrium sorption behavior of a specific compound–sediment combination is to be determined. It should be noted that column experiments always remain limited in their transferability to real-world conditions because of experimental restrictions (such as those imposed by limitations of scale), which means that processes that might occur simultaneously in nature cannot be fully reproduced in a laboratory. Column experiments therefore sometimes represent field conditions quite well and sometimes do not. However, the main objective of column experiments should not be specifically to achieve laboratory results that are transferable to real-world conditions, but to achieve an improved general understanding of the behavior of organic compounds, i.e., how different boundary conditions affect the behavior of the investigated compounds in natural environments.
Laboratory column experiments are a valuable and appropriate method for investigating and characterizing the transport behavior of organic micropollutants. They have been widely used in recent decades for numerous investigations into a great variety of organic compounds. This has led to an enormous increase in our understanding of this ever growing group of compounds. While the experimental method in general can now be considered a standard method, many different setups have been used, which is a major issue when it comes to comparing results. A standardized setup for column experiments would yield results that are fully comparable and transferable between different column experiments. It is of course not surprising that such a standardized setup does not exist, as the setup used invariably depends very much on the specific research question being investigated. Steps towards the standardization of column experiments could include following the suggestion by Mackay and Seremet (2008) that model substances be used for investigations into cation exchange, and the use of reference soils to characterize different compounds as suggested by Bi et al. (2006). These suggestions have to date only been applied to batch sorption experiments and not to column experiments. It would however be of great benefit if the research community could agree on specific reference compounds and substrates to use in column experiments, in order to help overcome the issue of comparability. This would not only facilitate comparisons between different experiments but also eventually bring us closer to achieving a more universal understanding of the transport and eventual fate of organic micropollutants in groundwater.
Column experiments can provide good estimates of almost all relevant transport parameters for a specific compound in a specific sediment–groundwater setting, such as the retardation (transport velocity compared to groundwater flow velocity), the underlying sorption–desorption processes, and the degradation (as mass loss). However, the results obtained will almost always be limited to the scale of the experiment, which means that they are unlikely to be directly transferable to a field scale as too many parameters will be exclusive to the laboratory column setup. The remaining challenge is therefore to develop standardized column experiments for organic micropollutants that will be able to overcome this issue.
The authors thank Martin Gitter for helping to compile the tables in the Supplement. This publication received financial support from the Carl-Fredrik von Horns Fund. Edited by: A. Guadagnini Reviewed by: four anonymous referees